Curriculum learning for printed text line recognition of ligature-based scripts

This paper introduces a novel curriculum learning strategy for ligature-based scripts. Long Short-Term Memory Networks require thousands or even millions of iterations on target symbols, depending upon the complexity of the target data, to converge when trained for sequence transcription because they have to localize the individual symbols along with the recognition. Curriculum learning reduces the number of target symbols to be visited before the network converges. In this paper, we propose a ligature-based complexity measure to define the sampling order of the training data. Experiments performed on UPTI database show that the curriculum learning using our strategy can reduce the total number of target symbols before convergence for printed Urdu Nastaleeq OCR task.